Federated learning: A comparison of methods

Federated learning: A comparison of methods: How do different ML models compare to each other

Sīpols, Emīls (TU Delft Electrical Engineering, Mathematics and Computer Science)

Garst, S.J.F. (mentor)
Reinders, M.J.T. (graduation committee)
Chen, Lydia Y. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and provide
practical insights for practitioners in the field. The findings highlight the performance and limitations of linear and non-linear models on MNIST and Ki-nase datasets.

Machine Learning
Federated Learning
Distributed Machine Learning

http://resolver.tudelft.nl/uuid:5aaccd90-2079-4dd6-8d5f-4387ff9c1bdf

Student theses

bachelor thesis

© 2023 Emīls Sīpols